Premium
Prediction Uncertainty in Seasonal Partial Duration Series
Author(s) -
Rasmussen Peter Funder,
Rosbjerg Dan
Publication year - 1991
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/91wr01731
Subject(s) - estimator , statistics , pooling , mathematics , series (stratigraphy) , seasonality , flood myth , event (particle physics) , mean squared error , econometrics , computer science , geography , paleontology , physics , archaeology , quantum mechanics , artificial intelligence , biology
In order to obtain a good description of the exceedances in a partial duration series it is often necessary to divide the year into a number (2–4) of seasons. Hereby a stationary exceedance distribution can be maintained within each season. This type of seasonal models may, however, not be suitable for prediction purposes due to the large number of parameters required. In the particular case with exponentially distributed exceedances and Poissonian occurrence times the precision of the T year event estimator has been thoroughly examined considering both seasonal and nonseasonal models. The two‐seasonal probability density function of the T year event estimator has been deduced and used in the assessment of the precision of approximate moments. The nonseasonal approach covered both a total omission of seasonality by pooling data from different flood seasons and a discarding of nonsignificant season(s) before the analysis of extremes. Mean square error approximations (bias second order, variance first and second order) were employed as measures for prediction uncertainty. It was found that optimal estimates can usually be obtained with a nonseasonal approach.